Summary of Hyperband-based Bayesian Optimization For Black-box Prompt Selection, by Lennart Schneider and Martin Wistuba and Aaron Klein and Jacek Golebiowski and Giovanni Zappella and Felice Antonio Merra
Hyperband-based Bayesian Optimization for Black-box Prompt Selection
by Lennart Schneider, Martin Wistuba, Aaron Klein, Jacek Golebiowski, Giovanni Zappella, Felice Antonio Merra
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces a novel approach to optimizing prompt selection for large language models (LLMs) in a black-box setting. The proposed method, called HbBoPs, combines a structural-aware deep kernel Gaussian Process with Hyperband as a multi-fidelity scheduler to select the most effective prompts. By leveraging the inherent structure of prompts and efficiently evaluating them on validation instances, HbBoPs outperforms state-of-the-art methods across ten benchmarks and three LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us understand how to get the best results from large language models by choosing the right prompts. Currently, people manually adjust prompts until they work well, but this can be time-consuming and not very effective. The new method, called HbBoPs, is better because it considers the different parts of a prompt (instructions and examples) and only evaluates them on the most important instances. This makes it faster and more accurate. |
Keywords
» Artificial intelligence » Prompt
